The Tidyverse

* __  _    __   .    o           *  . 
 / /_(_)__/ /_ ___  _____ _______ ___ 
/ __/ / _  / // / |/ / -_) __(_-</ -_)
\__/_/\_,_/\_, /|___/\__/_/ /___/\__/ 
     *  . /___/      o      .       * 

We are going to be working around in the tidyverse for a good chunk of our time together. The whole point of the tidyverse is to offer a grammar of verbs. It is going to help us in a lot of the situations that we are going to be seeing.

Another great feature of the tidyverse is the pipe: %>%

It does the same thing as the Unix |, but | in R is an or operator.

With all of the glowing praise for the tidyverse, we are still going to see some base R. Sometimes, it will demonstrate great reasons for using the tidyverse. In other situations, it will help you to not be afraid to use it when situations arise.

Some Demonstrations

Summary Tables

library(ggplot2)

plotDat = aggregate(diamonds$cut, by = list(cut = diamonds$cut), 
                    FUN = length)

colnames(plotDat)[2] = "n"

plotDat
        cut     n
1      Fair  1610
2      Good  4906
3 Very Good 12082
4   Premium 13791
5     Ideal 21551

Visual

ggplot(plotDat, aes(x = cut, y = n)) +
  geom_point(aes(size = n)) +
  theme_minimal()

(Im)Proper Plotting

Look at help(mtcars) and check out the variables. Can you spot what is wrong with this plot?

ggplot(mtcars, aes(x = wt, y = mpg, color = am)) + 
  geom_point() +
  theme_minimal()

Proper Plotting

The plot below is likely better.

library(dplyr)

mtcars$amFactor = as.factor(mtcars$am) 

ggplot(mtcars, aes(x = wt, y = mpg, color = amFactor)) + 
  geom_point() +
  theme_minimal()

Pipes: Making Life Easier

Recall some of the things that we just saw:

plotDat = aggregate(diamonds$cut, by = list(cut = diamonds$cut), FUN = length)

colnames(plotDat)[2] = "n"

ggplot(plotDat, aes(x = cut, y = n)) +
  geom_point(aes(size = n)) +
  theme_minimal()

This is somewhat tricky code. We have to create a new object with the oft-muddy aggregate and reset a column name (by magic number in an index, no less).

This can be made much easier with dplyr:

diamonds %>% 
  group_by(cut) %>% 
  summarize(n = n()) %>% 
  ggplot(., aes(x = cut, y = n)) +
  geom_point(aes(size = n)) +
  theme_minimal()

It isn’t a reduction in lines, but it is certainly clearer and follows a more logical thought process. This is the whole point of the tidyverse (and dplyr specifically) – allowing you to write how you would explain the process.

As an added bonus, we don’t need to create a bunch of different objects to do something simple.

We can see that dplyr will also make the plot for am easier.

mtcars %>% 
  mutate(am = as.factor(am)) %>%  
  ggplot(., aes(x = wt, y = mpg, color = am)) + 
  geom_point() +
  theme_minimal()

On Code Golf

You will often notice that a dplyr chunk might take a few more lines to work through than base R alone – don’t consider this as a bad thing. There will be many times in this course and in your potential work that you might think that you need to use as few lines as possible. Resist this temptation. Sometime you need to break something up into many lines and create new objects – this ability is exactly why we use R!

Data Import

Importing data is often the easiest part (never too hard to import a nice .csv). Sometimes, though, we need some other strategies.

Delimited Files

Frequently, you will see nicely delimited text files that are not .csv files – these are often tab-delimited files, but they can take other forms.

read.table("https://download.bls.gov/pub/time.series/ce/ce.data.42a.RetailTrade.Employment", 
           header = TRUE, sep = "\t")

Is the same as:

read.delim("https://download.bls.gov/pub/time.series/ce/ce.data.42a.RetailTrade.Employment")

The read.table() function gives you added flexibility to specify many different parameters.

Examine the following file from SDC Platinum and read it in properly:

SDC Wackiness

How did you do?

Did you notice anything about these files? They are not really very big, but they might have taken a little bit of time to read in. There have been times where people have commented that R is too slow on the read side. If you find you files are not being read quickly enough, you can try a few alternatives: readr and data.table

Try the following:

library(readr)

readrTest = read_delim("https://download.bls.gov/pub/time.series/ce/ce.data.42a.RetailTrade.Employment", 
                       delim = "\t")
library(data.table)

dtTest = fread("https://download.bls.gov/pub/time.series/ce/ce.data.42a.RetailTrade.Employment", 
               sep = "\t")

That SDC file that might have taken a few minutes will now take just a few seconds:

sdc = read_delim("https://www3.nd.edu/~sberry5/data/sdcTest.txt", 
                 delim = "^")

Pretty awesome, right?

While readr works wonderfully on the read and write side, data.table is great for wrangling data that is a bit on the big side and is all together blazing fast. However, it does not shy away from confusing syntax and weird conventions. With that in mind, we won’t be using it in this class, but do keep it in the back of your mind.

At times, you will get data in some proprietary format. That is when you need to turn to other places.

Excel

readxl::read_excel(path = "folder/fileName")

What do we know about Excel workbooks? Check out the help on readxl and let me know our path forward.

SAS

haven::read_sas(data_file = "https://www3.nd.edu/~sberry5/data/wciklink_gvkey.sas7bdat")

Stata

haven::read_dta(file = "https://www3.nd.edu/~sberry5/data/stataExample.dta")

SPSS

We often see the -99 added as the missing value in SPSS (of course, there is no way that -99 would ever be an actual value, right?).

haven::read_spss(file = "https://www3.nd.edu/~sberry5/data/spssExample.sav", 
                 user_na = "-99")

HTML

Depending on your needs, reading an html table into R is getting to be too easy.

library(rvest)

cpi = read_html("http://www.usinflationcalculator.com/inflation/consumer-price-index-and-annual-percent-changes-from-1913-to-2008/") %>% 
  html_table(fill = TRUE)

Things might get a bit tricky:

highest = read_html("https://en.wikipedia.org/wiki/List_of_highest-grossing_films") %>% 
  html_table(fill = TRUE)

What is the return of this call?

rio

For many of these tasks, you can just use the rio package – you give it the file and it will do the rest!

rio::import("folder/file")

Nested Structures

JSON

Web-based graphics started getting popular not too long ago. Generally, stats people were not using them, but web developer-type folks were. They needed a structure that would work well for the web and interact with their JavaScript-based graphics – thus, JavaScript Object Notation (JSON) was born. You will see JSON come out of many web-based interfaces.

This is what JSON looks like.

There are a few JSON-reading packages in R, but jsonlite tends to work pretty well.

jsonTest = jsonlite::read_json(path = "https://www3.nd.edu/~sberry5/data/optionsDataBrief.json", 
                                simplifyVector = TRUE)

This is a very simple form of JSON. We are going to see a hairier version of this data in the coming days.

JSON Dangers

There is JSON and then there is JSON. You might find yourself some interesting data and want to bring it in, but an error happens and you have no idea why the read_json function is telling you that the file is not JSON.

Not all JSON is pure JSON! When that is the case, you will need to create pure JSON.

Mass Reading

Everything we just learned is great and you will use them all in your data wrangling missions.

Fortunately (or unfortunately, depending on how you look at it), it is not the whole story – you will frequently be reading in many files of the same time.

If you have two files, you might be able to get away with brute force:

# DO NOT RUN:

myData1 = read.csv("test.csv")

myData2 = read.csv("test2.csv")

Would you want to do this for 5 files? What about 100? Or 1000? I will answer it for you: no!

The chunks below introduce some very important functions. We are going to see lapply again – it is important that you learn to love the apply family!

# DO NOT RUN:

allFiles = list.files(path = "", all.files = TRUE, full.names = TRUE, 
                      recursive = TRUE, include.dirs = FALSE)

allFilesRead = lapply(allFiles, function(x) read.csv(x, stringsAsFactors = FALSE))

allData = do.call("rbind", allFilesRead)

You can also use rio:

# DO NOT RUN:

rio::import_list("", rbind = TRUE)

The Grammar Of Data

One of the major aims of the tidyverse is to provide a clear and consistent grammar to data manipulation. This is helpful when diving deeper into the weeds.

Do you remember this?

highest = read_html("https://en.wikipedia.org/wiki/List_of_highest-grossing_films") %>% 
  html_table(fill = TRUE)

What did we get out of this? It was a big list of data frames. If we are looking for only one thing and we know that it is the first thing, we have some options:

highest = highest[[1]]

This is great for keeping the object at first and then plucking out what we want. If you want the whole thing to be together, though, we have even more options:

highest = read_html("https://en.wikipedia.org/wiki/List_of_highest-grossing_films") %>% 
  html_table(fill = TRUE) %>% 
  `[[`(1)

And now we see why R mystifies people. What does is that bit of nonsense at the end. It is really just an index shortcut. Once you know how to use it, it is great; however, it will make you shake your head if you see it in the wild without knowing about it first.

This is where the benefit of tidyverse becomes clear.

highest = read_html("https://en.wikipedia.org/wiki/List_of_highest-grossing_films") %>% 
  html_table(fill = TRUE) %>%
  magrittr::extract2(1)

Or…

highest = read_html("https://en.wikipedia.org/wiki/List_of_highest-grossing_films") %>% 
  html_table(fill = TRUE) %>%
  purrr::pluck(1)

Both functions are doing the same thing and with slightly different names, but it is crystal-clear what they are doing.

Do be careful, though, because we can have some issues in function masking and pluck from purrr does something very different than pluck from dplyr.

Someone try it and tell me what happens!

Selecting

Base

There are many ways to select variables with base R:

mtcars[, c(1:5, 7:8)]

keepers = c("mpg", "cyl", "disp", "hp", "drat", "qsec", "vs")

mtcars[, keepers]

mtcars[, c("mpg", grep("^c", names(mtcars), values = TRUE))]

You can also drop variables:

mtcars[, -c(1:2)]
                     disp  hp drat    wt  qsec vs am gear carb amFactor
Mazda RX4           160.0 110 3.90 2.620 16.46  0  1    4    4        1
Mazda RX4 Wag       160.0 110 3.90 2.875 17.02  0  1    4    4        1
Datsun 710          108.0  93 3.85 2.320 18.61  1  1    4    1        1
Hornet 4 Drive      258.0 110 3.08 3.215 19.44  1  0    3    1        0
Hornet Sportabout   360.0 175 3.15 3.440 17.02  0  0    3    2        0
Valiant             225.0 105 2.76 3.460 20.22  1  0    3    1        0
Duster 360          360.0 245 3.21 3.570 15.84  0  0    3    4        0
Merc 240D           146.7  62 3.69 3.190 20.00  1  0    4    2        0
Merc 230            140.8  95 3.92 3.150 22.90  1  0    4    2        0
Merc 280            167.6 123 3.92 3.440 18.30  1  0    4    4        0
Merc 280C           167.6 123 3.92 3.440 18.90  1  0    4    4        0
Merc 450SE          275.8 180 3.07 4.070 17.40  0  0    3    3        0
Merc 450SL          275.8 180 3.07 3.730 17.60  0  0    3    3        0
Merc 450SLC         275.8 180 3.07 3.780 18.00  0  0    3    3        0
Cadillac Fleetwood  472.0 205 2.93 5.250 17.98  0  0    3    4        0
Lincoln Continental 460.0 215 3.00 5.424 17.82  0  0    3    4        0
Chrysler Imperial   440.0 230 3.23 5.345 17.42  0  0    3    4        0
Fiat 128             78.7  66 4.08 2.200 19.47  1  1    4    1        1
Honda Civic          75.7  52 4.93 1.615 18.52  1  1    4    2        1
Toyota Corolla       71.1  65 4.22 1.835 19.90  1  1    4    1        1
Toyota Corona       120.1  97 3.70 2.465 20.01  1  0    3    1        0
Dodge Challenger    318.0 150 2.76 3.520 16.87  0  0    3    2        0
AMC Javelin         304.0 150 3.15 3.435 17.30  0  0    3    2        0
Camaro Z28          350.0 245 3.73 3.840 15.41  0  0    3    4        0
Pontiac Firebird    400.0 175 3.08 3.845 17.05  0  0    3    2        0
Fiat X1-9            79.0  66 4.08 1.935 18.90  1  1    4    1        1
Porsche 914-2       120.3  91 4.43 2.140 16.70  0  1    5    2        1
Lotus Europa         95.1 113 3.77 1.513 16.90  1  1    5    2        1
Ford Pantera L      351.0 264 4.22 3.170 14.50  0  1    5    4        1
Ferrari Dino        145.0 175 3.62 2.770 15.50  0  1    5    6        1
Maserati Bora       301.0 335 3.54 3.570 14.60  0  1    5    8        1
Volvo 142E          121.0 109 4.11 2.780 18.60  1  1    4    2        1
dropVars = c("vs", "drat")

mtcars[, !(names(mtcars) %in% dropVars)]
                     mpg cyl  disp  hp    wt  qsec am gear carb amFactor
Mazda RX4           21.0   6 160.0 110 2.620 16.46  1    4    4        1
Mazda RX4 Wag       21.0   6 160.0 110 2.875 17.02  1    4    4        1
Datsun 710          22.8   4 108.0  93 2.320 18.61  1    4    1        1
Hornet 4 Drive      21.4   6 258.0 110 3.215 19.44  0    3    1        0
Hornet Sportabout   18.7   8 360.0 175 3.440 17.02  0    3    2        0
Valiant             18.1   6 225.0 105 3.460 20.22  0    3    1        0
Duster 360          14.3   8 360.0 245 3.570 15.84  0    3    4        0
Merc 240D           24.4   4 146.7  62 3.190 20.00  0    4    2        0
Merc 230            22.8   4 140.8  95 3.150 22.90  0    4    2        0
Merc 280            19.2   6 167.6 123 3.440 18.30  0    4    4        0
Merc 280C           17.8   6 167.6 123 3.440 18.90  0    4    4        0
Merc 450SE          16.4   8 275.8 180 4.070 17.40  0    3    3        0
Merc 450SL          17.3   8 275.8 180 3.730 17.60  0    3    3        0
Merc 450SLC         15.2   8 275.8 180 3.780 18.00  0    3    3        0
Cadillac Fleetwood  10.4   8 472.0 205 5.250 17.98  0    3    4        0
Lincoln Continental 10.4   8 460.0 215 5.424 17.82  0    3    4        0
Chrysler Imperial   14.7   8 440.0 230 5.345 17.42  0    3    4        0
Fiat 128            32.4   4  78.7  66 2.200 19.47  1    4    1        1
Honda Civic         30.4   4  75.7  52 1.615 18.52  1    4    2        1
Toyota Corolla      33.9   4  71.1  65 1.835 19.90  1    4    1        1
Toyota Corona       21.5   4 120.1  97 2.465 20.01  0    3    1        0
Dodge Challenger    15.5   8 318.0 150 3.520 16.87  0    3    2        0
AMC Javelin         15.2   8 304.0 150 3.435 17.30  0    3    2        0
Camaro Z28          13.3   8 350.0 245 3.840 15.41  0    3    4        0
Pontiac Firebird    19.2   8 400.0 175 3.845 17.05  0    3    2        0
Fiat X1-9           27.3   4  79.0  66 1.935 18.90  1    4    1        1
Porsche 914-2       26.0   4 120.3  91 2.140 16.70  1    5    2        1
Lotus Europa        30.4   4  95.1 113 1.513 16.90  1    5    2        1
Ford Pantera L      15.8   8 351.0 264 3.170 14.50  1    5    4        1
Ferrari Dino        19.7   6 145.0 175 2.770 15.50  1    5    6        1
Maserati Bora       15.0   8 301.0 335 3.570 14.60  1    5    8        1
Volvo 142E          21.4   4 121.0 109 2.780 18.60  1    4    2        1

Issues?

For starters, the magic numbers are a no-go. The keepers lines could work, but would be a pain if we had a lot of variables.

Let’s check this wacky stuff out where we want all variables that start with “age” and variables that likely represent questions (x1, x2, x3, …):

library(lavaan)

testData = HolzingerSwineford1939

names(testData)
 [1] "id"     "sex"    "ageyr"  "agemo"  "school" "grade"  "x1"    
 [8] "x2"     "x3"     "x4"     "x5"     "x6"     "x7"     "x8"    
[15] "x9"    
keepers = c(grep("^age", names(testData), value = TRUE), 
            paste("x", 1:9, sep = ""))

testData = testData[, keepers]

Not only do we have another regular expression, but we also have this paste line to create variable names. It seems like too much work to do something simple!

While not beautiful, these are perfectly valid ways to do this work. I have such sights to show you, but don’t forget about this stuff – you never know when you might need to use it.

dplyr

We have already seen a bit of dplyr, but we are going to dive right into some of the functions now.

In base R, we have to do some chanting to select our variables. With dplyr, we can just use select:

mtcars %>% 
  select(mpg, cyl, am)
                     mpg cyl am
Mazda RX4           21.0   6  1
Mazda RX4 Wag       21.0   6  1
Datsun 710          22.8   4  1
Hornet 4 Drive      21.4   6  0
Hornet Sportabout   18.7   8  0
Valiant             18.1   6  0
Duster 360          14.3   8  0
Merc 240D           24.4   4  0
Merc 230            22.8   4  0
Merc 280            19.2   6  0
Merc 280C           17.8   6  0
Merc 450SE          16.4   8  0
Merc 450SL          17.3   8  0
Merc 450SLC         15.2   8  0
Cadillac Fleetwood  10.4   8  0
Lincoln Continental 10.4   8  0
Chrysler Imperial   14.7   8  0
Fiat 128            32.4   4  1
Honda Civic         30.4   4  1
Toyota Corolla      33.9   4  1
Toyota Corona       21.5   4  0
Dodge Challenger    15.5   8  0
AMC Javelin         15.2   8  0
Camaro Z28          13.3   8  0
Pontiac Firebird    19.2   8  0
Fiat X1-9           27.3   4  1
Porsche 914-2       26.0   4  1
Lotus Europa        30.4   4  1
Ford Pantera L      15.8   8  1
Ferrari Dino        19.7   6  1
Maserati Bora       15.0   8  1
Volvo 142E          21.4   4  1

We can also drop variables with the -:

mtcars %>% 
  select(-vs)
                     mpg cyl  disp  hp drat    wt  qsec am gear carb
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  1    4    4
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  1    4    4
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1    4    1
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0    3    2
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  0    3    1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0    3    4
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  0    4    2
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  0    4    2
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  0    4    4
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  0    4    4
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0    3    4
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1    4    1
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1    4    2
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1    4    1
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  0    3    1
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0    3    2
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1    4    1
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  1    5    2
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1    5    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  1    5    4
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  1    5    6
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  1    5    8
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1    4    2
                    amFactor
Mazda RX4                  1
Mazda RX4 Wag              1
Datsun 710                 1
Hornet 4 Drive             0
Hornet Sportabout          0
Valiant                    0
Duster 360                 0
Merc 240D                  0
Merc 230                   0
Merc 280                   0
Merc 280C                  0
Merc 450SE                 0
Merc 450SL                 0
Merc 450SLC                0
Cadillac Fleetwood         0
Lincoln Continental        0
Chrysler Imperial          0
Fiat 128                   1
Honda Civic                1
Toyota Corolla             1
Toyota Corona              0
Dodge Challenger           0
AMC Javelin                0
Camaro Z28                 0
Pontiac Firebird           0
Fiat X1-9                  1
Porsche 914-2              1
Lotus Europa               1
Ford Pantera L             1
Ferrari Dino               1
Maserati Bora              1
Volvo 142E                 1

We also have several helper functions that we can use:

HolzingerSwineford1939 %>% 
  select(num_range("x", 1:9), starts_with("age"), 
         matches("^s.*.l$"))

Not Important, But Helpful

Changing variable position in R is a pain:

head(HolzingerSwineford1939[, c(1, 7:15, 2:6)])
  id       x1   x2    x3       x4   x5        x6       x7   x8       x9
1  1 3.333333 7.75 0.375 2.333333 5.75 1.2857143 3.391304 5.75 6.361111
2  2 5.333333 5.25 2.125 1.666667 3.00 1.2857143 3.782609 6.25 7.916667
3  3 4.500000 5.25 1.875 1.000000 1.75 0.4285714 3.260870 3.90 4.416667
4  4 5.333333 7.75 3.000 2.666667 4.50 2.4285714 3.000000 5.30 4.861111
5  5 4.833333 4.75 0.875 2.666667 4.00 2.5714286 3.695652 6.30 5.916667
6  6 5.333333 5.00 2.250 1.000000 3.00 0.8571429 4.347826 6.65 7.500000
  sex ageyr agemo  school grade
1   1    13     1 Pasteur     7
2   2    13     7 Pasteur     7
3   2    13     1 Pasteur     7
4   1    13     2 Pasteur     7
5   2    12     2 Pasteur     7
6   2    14     1 Pasteur     7
HolzingerSwineford1939 %>% 
  select(id, starts_with("x"), everything()) %>% 
  head()
  id       x1   x2    x3       x4   x5        x6       x7   x8       x9
1  1 3.333333 7.75 0.375 2.333333 5.75 1.2857143 3.391304 5.75 6.361111
2  2 5.333333 5.25 2.125 1.666667 3.00 1.2857143 3.782609 6.25 7.916667
3  3 4.500000 5.25 1.875 1.000000 1.75 0.4285714 3.260870 3.90 4.416667
4  4 5.333333 7.75 3.000 2.666667 4.50 2.4285714 3.000000 5.30 4.861111
5  5 4.833333 4.75 0.875 2.666667 4.00 2.5714286 3.695652 6.30 5.916667
6  6 5.333333 5.00 2.250 1.000000 3.00 0.8571429 4.347826 6.65 7.500000
  sex ageyr agemo  school grade
1   1    13     1 Pasteur     7
2   2    13     7 Pasteur     7
3   2    13     1 Pasteur     7
4   1    13     2 Pasteur     7
5   2    12     2 Pasteur     7
6   2    14     1 Pasteur     7

Your Turn!

  1. Use that Stata test file.

  2. Grab every lvi, effect, leader, and cred variable

  3. Use summary to understand your data.

  4. Now, just keep every lvi variable.

  5. Use a corrplot to see relationships.

    • corrplot needs a correlation matrix (use cor)
# Just to give you an idea about how it works!

install.packages("corrplot")

data.frame(x = rnorm(10), y = rnorm(10)) %>% 
  cor() %>% 
  corrplot()

Subsetting/Filtering

One of the more frequent tasks is related to filtering/subsetting your data. You often want to impose some types of rules on your data (e.g., US only, date ranges).

Base

R gives us all the ability in the world to filter data.

summary(mtcars[mtcars$mpg < mean(mtcars$mpg), ])
      mpg             cyl             disp             hp       
 Min.   :10.40   Min.   :6.000   Min.   :145.0   Min.   :105.0  
 1st Qu.:14.78   1st Qu.:8.000   1st Qu.:275.8   1st Qu.:156.2  
 Median :15.65   Median :8.000   Median :311.0   Median :180.0  
 Mean   :15.90   Mean   :7.556   Mean   :313.8   Mean   :191.9  
 3rd Qu.:18.02   3rd Qu.:8.000   3rd Qu.:360.0   3rd Qu.:226.2  
 Max.   :19.70   Max.   :8.000   Max.   :472.0   Max.   :335.0  
      drat             wt             qsec             vs        
 Min.   :2.760   Min.   :2.770   Min.   :14.50   Min.   :0.0000  
 1st Qu.:3.070   1st Qu.:3.440   1st Qu.:16.10   1st Qu.:0.0000  
 Median :3.150   Median :3.570   Median :17.35   Median :0.0000  
 Mean   :3.302   Mean   :3.839   Mean   :17.10   Mean   :0.1667  
 3rd Qu.:3.600   3rd Qu.:3.844   3rd Qu.:17.94   3rd Qu.:0.0000  
 Max.   :4.220   Max.   :5.424   Max.   :20.22   Max.   :1.0000  
       am              gear            carb       amFactor
 Min.   :0.0000   Min.   :3.000   Min.   :1.000   0:15    
 1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.250   1: 3    
 Median :0.0000   Median :3.000   Median :4.000           
 Mean   :0.1667   Mean   :3.444   Mean   :3.556           
 3rd Qu.:0.0000   3rd Qu.:3.750   3rd Qu.:4.000           
 Max.   :1.0000   Max.   :5.000   Max.   :8.000           

Unless you know exactly what you are doing, this is a bit hard to read – you might be asking yourself what the comma means and why there is nothing after it.

dplyr

When we use filter, we are specifying what it is that we want to keep.

Keep this or that:

mtcars %>% 
  filter(cyl == 4 | cyl == 8) %>% 
  summary()
      mpg             cyl            disp             hp       
 Min.   :10.40   Min.   :4.00   Min.   : 71.1   Min.   : 52.0  
 1st Qu.:15.20   1st Qu.:4.00   1st Qu.:120.1   1st Qu.: 93.0  
 Median :18.70   Median :8.00   Median :275.8   Median :150.0  
 Mean   :20.19   Mean   :6.24   Mean   :244.0   Mean   :153.5  
 3rd Qu.:24.40   3rd Qu.:8.00   3rd Qu.:351.0   3rd Qu.:205.0  
 Max.   :33.90   Max.   :8.00   Max.   :472.0   Max.   :335.0  
      drat            wt             qsec             vs     
 Min.   :2.76   Min.   :1.513   Min.   :14.50   Min.   :0.0  
 1st Qu.:3.08   1st Qu.:2.320   1st Qu.:16.90   1st Qu.:0.0  
 Median :3.69   Median :3.435   Median :17.60   Median :0.0  
 Mean   :3.60   Mean   :3.245   Mean   :17.81   Mean   :0.4  
 3rd Qu.:4.08   3rd Qu.:3.780   3rd Qu.:18.61   3rd Qu.:1.0  
 Max.   :4.93   Max.   :5.424   Max.   :22.90   Max.   :1.0  
       am           gear           carb      amFactor
 Min.   :0.0   Min.   :3.00   Min.   :1.00   0:15    
 1st Qu.:0.0   1st Qu.:3.00   1st Qu.:2.00   1:10    
 Median :0.0   Median :3.00   Median :2.00           
 Mean   :0.4   Mean   :3.64   Mean   :2.64           
 3rd Qu.:1.0   3rd Qu.:4.00   3rd Qu.:4.00           
 Max.   :1.0   Max.   :5.00   Max.   :8.00           

Keep this and that:

mtcars %>% 
  filter(cyl == 4 & mpg > 25) %>% 
  summary()
      mpg             cyl         disp              hp        
 Min.   :26.00   Min.   :4   Min.   : 71.10   Min.   : 52.00  
 1st Qu.:28.07   1st Qu.:4   1st Qu.: 76.45   1st Qu.: 65.25  
 Median :30.40   Median :4   Median : 78.85   Median : 66.00  
 Mean   :30.07   Mean   :4   Mean   : 86.65   Mean   : 75.50  
 3rd Qu.:31.90   3rd Qu.:4   3rd Qu.: 91.08   3rd Qu.: 84.75  
 Max.   :33.90   Max.   :4   Max.   :120.30   Max.   :113.00  
      drat             wt             qsec             vs        
 Min.   :3.770   Min.   :1.513   Min.   :16.70   Min.   :0.0000  
 1st Qu.:4.080   1st Qu.:1.670   1st Qu.:17.30   1st Qu.:1.0000  
 Median :4.150   Median :1.885   Median :18.71   Median :1.0000  
 Mean   :4.252   Mean   :1.873   Mean   :18.40   Mean   :0.8333  
 3rd Qu.:4.378   3rd Qu.:2.089   3rd Qu.:19.33   3rd Qu.:1.0000  
 Max.   :4.930   Max.   :2.200   Max.   :19.90   Max.   :1.0000  
       am         gear            carb     amFactor
 Min.   :1   Min.   :4.000   Min.   :1.0   0:0     
 1st Qu.:1   1st Qu.:4.000   1st Qu.:1.0   1:6     
 Median :1   Median :4.000   Median :1.5           
 Mean   :1   Mean   :4.333   Mean   :1.5           
 3rd Qu.:1   3rd Qu.:4.750   3rd Qu.:2.0           
 Max.   :1   Max.   :5.000   Max.   :2.0           

Filter this out:

mtcars %>% 
  filter(cyl != 4) %>% 
  summary()
      mpg             cyl             disp             hp       
 Min.   :10.40   Min.   :6.000   Min.   :145.0   Min.   :105.0  
 1st Qu.:15.00   1st Qu.:6.000   1st Qu.:225.0   1st Qu.:123.0  
 Median :16.40   Median :8.000   Median :301.0   Median :175.0  
 Mean   :16.65   Mean   :7.333   Mean   :296.5   Mean   :180.2  
 3rd Qu.:19.20   3rd Qu.:8.000   3rd Qu.:360.0   3rd Qu.:215.0  
 Max.   :21.40   Max.   :8.000   Max.   :472.0   Max.   :335.0  
      drat             wt             qsec             vs        
 Min.   :2.760   Min.   :2.620   Min.   :14.50   Min.   :0.0000  
 1st Qu.:3.070   1st Qu.:3.435   1st Qu.:16.46   1st Qu.:0.0000  
 Median :3.150   Median :3.520   Median :17.30   Median :0.0000  
 Mean   :3.348   Mean   :3.705   Mean   :17.17   Mean   :0.1905  
 3rd Qu.:3.730   3rd Qu.:3.840   3rd Qu.:17.98   3rd Qu.:0.0000  
 Max.   :4.220   Max.   :5.424   Max.   :20.22   Max.   :1.0000  
       am              gear            carb       amFactor
 Min.   :0.0000   Min.   :3.000   Min.   :1.000   0:16    
 1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000   1: 5    
 Median :0.0000   Median :3.000   Median :4.000           
 Mean   :0.2381   Mean   :3.476   Mean   :3.476           
 3rd Qu.:0.0000   3rd Qu.:4.000   3rd Qu.:4.000           
 Max.   :1.0000   Max.   :5.000   Max.   :8.000           

Naturally, it can also take a function

mtcars %>% 
  filter(mpg < mean(mpg)) %>% 
  summary()
      mpg             cyl             disp             hp       
 Min.   :10.40   Min.   :6.000   Min.   :145.0   Min.   :105.0  
 1st Qu.:14.78   1st Qu.:8.000   1st Qu.:275.8   1st Qu.:156.2  
 Median :15.65   Median :8.000   Median :311.0   Median :180.0  
 Mean   :15.90   Mean   :7.556   Mean   :313.8   Mean   :191.9  
 3rd Qu.:18.02   3rd Qu.:8.000   3rd Qu.:360.0   3rd Qu.:226.2  
 Max.   :19.70   Max.   :8.000   Max.   :472.0   Max.   :335.0  
      drat             wt             qsec             vs        
 Min.   :2.760   Min.   :2.770   Min.   :14.50   Min.   :0.0000  
 1st Qu.:3.070   1st Qu.:3.440   1st Qu.:16.10   1st Qu.:0.0000  
 Median :3.150   Median :3.570   Median :17.35   Median :0.0000  
 Mean   :3.302   Mean   :3.839   Mean   :17.10   Mean   :0.1667  
 3rd Qu.:3.600   3rd Qu.:3.844   3rd Qu.:17.94   3rd Qu.:0.0000  
 Max.   :4.220   Max.   :5.424   Max.   :20.22   Max.   :1.0000  
       am              gear            carb       amFactor
 Min.   :0.0000   Min.   :3.000   Min.   :1.000   0:15    
 1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.250   1: 3    
 Median :0.0000   Median :3.000   Median :4.000           
 Mean   :0.1667   Mean   :3.444   Mean   :3.556           
 3rd Qu.:0.0000   3rd Qu.:3.750   3rd Qu.:4.000           
 Max.   :1.0000   Max.   :5.000   Max.   :8.000           

Your Turn

For now, we are going to stick with that stataExample data.

  1. Select the same variables, but also include Rater.

  2. Filter the data on Rater – check the values and filter both ways.

  3. Now check those correlations again!

  4. Throw the Gender variable in and filter on that.

New Variables and Recoding

Base

Adding a new variable in base R is as easy as the following:

mtcars$roundedMPG = round(mtcars$mpg)

dplyr

If, however, we want to do things in a tidy chunk, we need to use mutate.

mtcars = mtcars %>% 
  mutate(roundedMPG = round(mpg))

There is also transmute. Can anyone venture a guess as to what it might do?

Base Recoding

You will need to recode variables at some point. Depending on the nature of the recode it can be easy (e.g., to reverse code a scale, you just subtract every value by max value + 1).

You will need to do some more elaborate stuff:

mtcars$mpgLoHi = 0

mtcars$mpgLoHi[mtcars$mpg > median(mtcars$mpg)] = 1
mtcars$mpgLoHi = ifelse(mtcars$mpg > median(mtcars$mpg), 1, 0)

These are pretty good ways to do recoding of this nature, but what about this:

mtcars$vs[which(mtcars$vs == 0)] = "v"

mtcars$vs[which(mtcars$vs == 1)] = "s"

Or this:

mtcars$vs = ifelse(mtcars$vs == 0, "v", "s")

dplyr recoding

recode(mtcars$vs, `0` = "v", `1` = "s")

Your Turn!

  1. For the sake of demonstration, select only the first 10 lvi variables and everything else.

  2. Keep only observations with Rater == 0.

  3. Assume that the first 5 lvi variables (01 through 05) are scores for one assessment and the next five (06 through 10) are scores for another assessment.

  4. Create two new variables to capture the mean of those scores.

  • You will need to use the rowwise function ahead of mutate.

  • You can use the mean function, but you will have to wrap the variables in c()

# Just to help you along!

data.frame(x = rnorm(10), y = rnorm(10)) %>% 
  rowwise() %>% 
  mutate(test = mean(c(x, y)))

Communication

We won’t have any big end-of-day wrap exercises to do today. Instead, we are going to learn just a few cool things.

ggplot2

We already saw some ggplot2, but let’s take a few minutes to dive into it a bit more.

Just like everything else in the tidyverse, ggplot2 provides a clear and consistent grammar, except the focus is on data visualization. With ggplot2, we can stack layer after layer into the plotting space to help visualize our data.

Let’s take a look at some good ggplot2 layering:

library(ggplot2)

library(lavaan)

testData = HolzingerSwineford1939

ggplot(testData, aes(x7, ageyr)) +
  geom_point()

Next, we can add some color:

ggplot(testData, aes(x7, ageyr)) +
  geom_point(aes(color = as.factor(grade)), alpha = .75)

Now, we can add a smooth line:

ggplot(testData, aes(x7, ageyr)) +
  geom_point(aes(color = as.factor(grade)), alpha = .75) + 
  geom_smooth()

And we can look at small multiples:

ggplot(testData, aes(x7, ageyr)) +
  geom_point(aes(color = as.factor(grade)), alpha = .75) + 
  geom_smooth() +
  facet_grid(~ sex)

Let’s get those silly grey boxes out of there:

ggplot(testData, aes(x7, ageyr)) +
  geom_point(aes(color = as.factor(grade)), alpha = .75) + 
  geom_smooth() +
  facet_grid(~ sex) +
  theme_minimal()

Perhaps add a better color scheme:

ggplot(testData, aes(x7, ageyr)) +
  geom_point(aes(color = as.factor(grade)), alpha = .75) + 
  geom_smooth() +
  facet_grid(~ sex) +
  theme_minimal() +
  scale_color_brewer(palette = "Dark2")

We could keep going forever and tweak anything that you could imagine (labels, ticks, etc.), but this should give you a pretty good idea about what you can do with regard to static plots.

Oh…but we don’t have to stick with just static plots. We can use the plotly package to make our ggplot object interactive.

library(plotly)

radPlot = ggplot(testData, aes(x7, ageyr)) +
  geom_point(aes(color = as.factor(grade)), alpha = .75) + 
  geom_smooth() +
  facet_grid(~ sex) +
  theme_minimal() +
  scale_color_brewer(palette = "Dark2")

ggplotly(radPlot)

You can also build plots with plotly, but we will save that for another day in the future.

Learning to use ggplot2 will pay great dividends – there is absolutely nothing better for creating visualizations. There is even a whole group of packages that do nothing but add stuff into it.

DT

Visualizations are great and they often tell a better story than tables. Sometimes, though, you want to give people a glimpse of the data. The DT package let’s you create interactive data tables (they are JS data tables).

You could give people the entire data to explore:

library(DT)

datatable(testData)

You can also use the DT package to tidy your summaries into a nice data frame:

lm(x7 ~ ageyr + school, data = testData) %>% 
  broom::tidy() %>% 
  mutate_if(is.numeric, round, 4) %>% 
  datatable()

We don’t want to get too far ahead of ourselves here – we will see more places to use this tomorrow.

R Markdown & Knitr

Do you have a moment to hear the good word of Donald Knuth? If you want to work in a reproducible fashion base and knitr are here to help you out. The slides you saw earlier and even the document you are seeing now are all done with R Markdown. It is my hope that you will also use R Markdown for your presentations on Thursday.

Day 1 Thought Question

Since we used the Stata stuff, let’s keep rolling with that. The Rater variable indicates whether the person is a supervisor (0) or a subordinate (3). Since this data comes from a bigger set, this coding might make sense – it makes no sense for the data at hand. I don’t believe that you would ever do this, but someone wants the leader_age variable discretized into two groups – below or at the mean and above the mean. The same goes with leader_tenure and leader_experience. In addition to these changes, someone is nervous about having both raterNum and leaderID available in the data; they are requesting that at least one of them be removed.

We have a few distinct issues to address within this data – what would you propose that we do?